Neural Dynamic Data Valuation: A Stochastic Optimal Control Approach

TL;DR

This paper introduces Neural Dynamic Data Valuation (NDDV), a novel framework that models data value over time as a stochastic optimal control problem, improving fairness, interpretability, and scalability in data valuation.

Contribution

It proposes a dynamic, trajectory-based approach to data valuation using stochastic optimal control, addressing limitations of existing static methods.

Findings

Reduces computational costs compared to traditional methods.

Enhances fairness and interpretability of data valuation.

Captures dynamic data utility evolution over time.

Abstract

Data valuation has become a cornerstone of the modern data economy, where datasets function as tradable intellectual assets that drive decision-making, model training, and market transactions. Despite substantial progress, existing valuation methods remain limited by high computational cost, weak fairness guarantees, and poor interpretability, which hinder their deployment in large-scale, high-stakes applications. This paper introduces Neural Dynamic Data Valuation (NDDV), a new framework that formulates data valuation as a stochastic optimal control problem to capture the dynamic evolution of data utility over time. Unlike static combinatorial approaches, NDDV models data interactions through continuous trajectories that reflect both individual and collective learning dynamics.